How to Map Object/JSON Array in Snowflake SQL / DBT Macro

Learn how to effectively map an `object/json array` in Snowflake SQL and DBT Macro to streamline your data cleaning process.
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Mapping Object/JSON Array in Snowflake SQL / DBT Macro

Data management is crucial in any data engineering initiative. When dealing with JSON arrays in Snowflake SQL, you might encounter the need to extract values while transforming your data. One common scenario occurs during the data cleaning phase, where you may want to simplify your dataset for further analysis. In this guide, we will discuss how to efficiently map object/json arrays in Snowflake SQL to achieve a cleaner, more usable output.

The Problem: Transforming JSON Arrays

Consider the following scenario: you have a JSON array in your table that contains objects with a specific attribute. For instance:

idsome_attributejson_array1"abc"[ { attr: 'apple'}, { attr: 'banana' } ]The goal is to convert the json_array so that it is less complex and directly presents the values of interest, yielding a result like this:

idsome_attributestring_array1"abc"[ 'apple', 'banana' ]Why Simplify the Data?

Simplifying your data at the cleaning stage reduces complexity in later processes. This enables quicker analyses and prevents potential errors when using complex data structures. By effectively mapping these JSON arrays, you can ensure that your data is ready for further stages in your pipeline.

The Solution: Utilizing a JavaScript UDF

Step 1: Create the UDF

To transform the JSON array, you can create a User Defined Function (UDF) in Snowflake using JavaScript. This function will specifically target the attr property in your JSON objects. Here's how you can set it up:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Implement the UDF in Your SQL Query

Once your UDF is created, you can apply it within your SQL query as follows:

[[See Video to Reveal this Text or Code Snippet]]

Expected Result

When this query is executed, you will receive the following output:

IDSOME_ATTRIBUTESTRING_ARRAY1abc[ "apple", "banana" ]This transformation effectively cleans up your data, providing a simpler format that is easier to work with in subsequent analysis.

Conclusion

By creating a JavaScript UDF to handle JSON arrays in Snowflake SQL, you can greatly simplify the process of mapping and transforming data for clean and efficient analysis. Now, you'll have a cleaner string_array that streamlines your data projects and enhances overall data quality.

Implement this solution and watch your data pipeline processes become more straightforward and manageable. Happy querying!